AI Blitz 5 ⚡

Image colorization with U-NET GAN

Step 1: Preparation¶

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# Getting data
!pip install git+https://gitlab.aicrowd.com/yoogottamk/aicrowd-cli.git
from aicrowd.magic import *
!aicrowd login --api-key <key>
!aicrowd dataset download --jobs 5 --challenge imgcol *v2.zip

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# Moving things into the right locations
!rm -rf data
!mkdir data

!mv 'test_black_white_images-v2.zip ' test_black_white_images-v2.zip
!mv 'validation_black_white_images-v2.zip ' validation_black_white_images-v2.zip

!unzip train_black_white_images-v2.zip -d data/
!unzip train_color_images-v2.zip -d data/
!unzip validation_black_white_images-v2.zip -d data/
!unzip validation_color_images-v2.zip -d data/
!unzip test_black_white_images-v2.zip -d data/

Import modules¶

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import numpy as np
import pandas as pd
from glob import glob
from os.path import join
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
from skimage.color import *
import tensorflow as tf

Data Preparation¶

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# Important parameters
img_size = 512

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# Get filepath to all images
fimgs_tr_x = sorted(glob('data/train_black_white_images-v2/*'))
fimgs_tr_y = sorted(glob('data/train_color_images-v2/*'))
fimgs_va_x = sorted(glob('data/validation_black_white_images-v2/*'))
fimgs_va_y = sorted(glob('data/validation_color_images-v2/*'))
fimgs_te_x = sorted(glob('data/test_black_white_images-v2/*'))

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# Combine train and validation set
fx_train = fimgs_tr_x + fimgs_va_x
fy_train = fimgs_tr_y + fimgs_va_y

Note!!!¶

Most of the following code is originally coming from this medium post. I've adapted them slighty and changed some of the NN architecture but credit to it goes clearly to the original auhtor.

2. The GAN¶

In this section, we'll create our GAN model step-by-step with Keras. First, we'll implement the generator then the discriminator and finally the loss functions required by both of them.

A. Generator¶

Our generator ( represented as $G$ ) will take in grayscale image $x$ and produce a LAB image $G( x )$. Note, $x$ will be a tensor of shape $( \ batch \ size \ , \ 512 \ , \ 512 \ , \ 1 \ )$ and the output $G(x)$ will have a shape $( \ batch \ size \ , \ 512 \ , \ 512 \ , \ 3 \ )$

Our generator will have a encoder-decoder structure, similar to the UNet architecture. Additionally, we use Dilated convolutions to have a larger receptive field.
We introduce skip connections in our model so as to have better flow of information from the encoder to the decoder.

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from tensorflow.keras.layers import (
    BatchNormalization, Conv2D, Conv2DTranspose, Dense,
    Flatten, Dropout, UpSampling2D, Concatenate, ELU,
    Input, LeakyReLU, MaxPooling2D, Reshape, UpSampling2D)

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def get_generator_model(ndim=32):

    #activation = LeakyReLU
    activation = ELU

    inputs = Input(shape=(img_size, img_size, 1))

    conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    pool1 = MaxPooling2D()(conv1)

    conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    pool2 = MaxPooling2D()(conv2)

    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = MaxPooling2D()(conv3)
    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    pool3 = MaxPooling2D()(conv3)

    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    conv4 = MaxPooling2D()(conv4)
    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D()(drop4)

    conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = BatchNormalization()(conv5)
    conv5 = activation()(conv5)
    conv5 = MaxPooling2D()(conv5)
    conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv5)
    conv5 = BatchNormalization()(conv5)
    conv5 = activation()(conv5)
    drop5 = Dropout(0.5)(conv5)

    bottleneck = Flatten()(drop5)
    bottleneck = Dense(1024)(bottleneck)
    bottleneck = Dense(ndim*256)(bottleneck)
    bottleneck = Reshape((4, 4, 16*ndim))(bottleneck)

    conv_up_1 = Conv2DTranspose(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(bottleneck)
    merge_1 = Concatenate()([conv5, conv_up_1])
    conv_up_1 = BatchNormalization()(merge_1)
    conv_up_1 = activation()(conv_up_1)
    conv_up_1 = UpSampling2D()(conv_up_1)
    conv_up_1 = Conv2DTranspose(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
    conv_up_1 = BatchNormalization()(conv_up_1)
    conv_up_1 = activation()(conv_up_1)
    conv_up_1 = UpSampling2D()(conv_up_1)

    conv_up_2 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
    merge_2 = Concatenate()([conv4, conv_up_2])
    conv_up_2 = BatchNormalization()(merge_2)
    conv_up_2 = activation()(conv_up_2)
    conv_up_2 = UpSampling2D()(conv_up_2)
    conv_up_2 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
    conv_up_2 = BatchNormalization()(conv_up_2)
    conv_up_2 = activation()(conv_up_2)
    conv_up_2 = UpSampling2D()(conv_up_2)

    conv_up_3 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
    merge_3 = Concatenate()([conv3, conv_up_3])
    conv_up_3 = BatchNormalization()(merge_3)
    conv_up_3 = activation()(conv_up_3)
    conv_up_3 = UpSampling2D()(conv_up_3)
    conv_up_3 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
    conv_up_3 = BatchNormalization()(conv_up_3)
    conv_up_3 = activation()(conv_up_3)
    conv_up_3 = UpSampling2D()(conv_up_3)

    conv_up_4 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
    merge_4 = Concatenate()([conv2, conv_up_4])
    conv_up_4 = BatchNormalization()(merge_4)
    conv_up_4 = activation()(conv_up_4)
    conv_up_4 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
    conv_up_4 = BatchNormalization()(conv_up_4)
    conv_up_4 = activation()(conv_up_4)
    conv_up_4 = UpSampling2D()(conv_up_4)

    conv_up_5 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
    merge_5 = Concatenate()([conv1, conv_up_5])
    conv_up_5 = BatchNormalization()(merge_5)
    conv_up_5 = activation()(conv_up_5)
    conv_up_5 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_5)
    conv_up_5 = BatchNormalization()(conv_up_5)
    conv_up_5 = activation()(conv_up_5)

    outputs = Conv2DTranspose(3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_5)

    model = tf.keras.models.Model(inputs, outputs)

    return model

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def get_generator_model(ndim=32):

    #activation = LeakyReLU
    activation = ELU

    inputs = Input(shape=(img_size, img_size, 1))

    conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = MaxPooling2D()(conv1)
    conv1 = Conv2D(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    pool1 = MaxPooling2D()(conv1)

    conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = MaxPooling2D()(conv2)
    conv2 = Conv2D(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    pool2 = MaxPooling2D()(conv2)

    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = MaxPooling2D()(conv3)
    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    pool3 = MaxPooling2D()(conv3)

    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    conv4 = MaxPooling2D()(conv4)
    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    drop4 = Dropout(0.5)(conv4)

    bottleneck = Flatten()(drop4)
    bottleneck = Dense(1024)(bottleneck)
    bottleneck = Dense(ndim*128)(bottleneck)
    bottleneck = Reshape((4, 4, 8*ndim))(bottleneck)

    conv_up_1 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(bottleneck)
    merge_1 = Concatenate()([conv4, conv_up_1])
    conv_up_1 = BatchNormalization()(merge_1)
    conv_up_1 = activation()(conv_up_1)
    conv_up_1 = UpSampling2D()(conv_up_1)
    conv_up_1 = Conv2DTranspose(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
    conv_up_1 = BatchNormalization()(conv_up_1)
    conv_up_1 = activation()(conv_up_1)
    conv_up_1 = UpSampling2D()(conv_up_1)

    conv_up_2 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_1)
    merge_2 = Concatenate()([conv3, conv_up_2])
    conv_up_2 = BatchNormalization()(merge_2)
    conv_up_2 = activation()(conv_up_2)
    conv_up_2 = UpSampling2D()(conv_up_2)
    conv_up_2 = Conv2DTranspose(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
    conv_up_2 = BatchNormalization()(conv_up_2)
    conv_up_2 = activation()(conv_up_2)
    conv_up_2 = UpSampling2D()(conv_up_2)

    conv_up_3 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_2)
    merge_3 = Concatenate()([conv2, conv_up_3])
    conv_up_3 = BatchNormalization()(merge_3)
    conv_up_3 = activation()(conv_up_3)
    conv_up_3 = UpSampling2D()(conv_up_3)
    conv_up_3 = Conv2DTranspose(ndim*2**1, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
    conv_up_3 = BatchNormalization()(conv_up_3)
    conv_up_3 = activation()(conv_up_3)
    conv_up_3 = UpSampling2D()(conv_up_3)

    conv_up_4 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_3)
    merge_4 = Concatenate()([conv1, conv_up_4])
    conv_up_4 = BatchNormalization()(merge_4)
    conv_up_4 = activation()(conv_up_4)
    conv_up_4 = UpSampling2D()(conv_up_4)
    conv_up_4 = Conv2DTranspose(ndim*2**0, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)
    conv_up_4 = BatchNormalization()(conv_up_4)
    conv_up_4 = activation()(conv_up_4)

    outputs = Conv2DTranspose(3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv_up_4)

    model = tf.keras.models.Model(inputs, outputs)

    return model

B. Discriminator¶

The discriminator model, represented as $D$, will take in the real image $y$ ( from the training data ) and the generated image $G(x)$ ( from the generator ) to output two probabilities.

We train the discriminator in such a manner that is able to differentiate the real images and the generated images. So, we train the model such that $y$ produces a output of $1.0$ and $G(x)$ produces an output of $0.0$.
Note that instead of using hard labels like $1.0$ and $0.0$, we use soft labels which are close to 1 and 0. So for a hard label of $1.0$, the soft label will be $(1 - \epsilon)$ where $\epsilon$ is picked uniformly from $( 0 , 0.1 ]$

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def get_discriminator_model(ndim=64):

    #activation = LeakyReLU
    activation = ELU

    inputs = Input(shape=(img_size, img_size, 3))

    conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=2, padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=2, padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = MaxPooling2D()(conv1)

    conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=2, padding='same', kernel_initializer='he_normal')(conv1)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=2, padding='same', kernel_initializer='he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = MaxPooling2D()(conv2)

    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = MaxPooling2D()(conv3)

    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    conv4 = MaxPooling2D()(conv4)

    conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
    conv5 = BatchNormalization()(conv5)
    conv5 = activation()(conv5)
    conv5 = Conv2D(ndim*2**4, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv5)
    conv5 = BatchNormalization()(conv5)
    conv5 = activation()(conv5)

    fully = Flatten()(conv5)
    fully = Dense(512)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(128)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(32)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(1, activation='sigmoid')(fully)

    model = tf.keras.models.Model(inputs, fully)

    return model

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def get_discriminator_model(ndim=64):

    #activation = LeakyReLU
    activation = ELU

    inputs = Input(shape=(img_size, img_size, 3))

    conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=1, padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = MaxPooling2D()(conv1)
    conv1 = Conv2D(ndim*2**0, kernel_size=(5, 5), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
    conv1 = BatchNormalization()(conv1)
    conv1 = activation()(conv1)
    conv1 = MaxPooling2D()(conv1)

    conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=1, padding='same', kernel_initializer='he_normal')(conv1)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = MaxPooling2D()(conv2)
    conv2 = Conv2D(ndim*2**1, kernel_size=(4, 4), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
    conv2 = BatchNormalization()(conv2)
    conv2 = activation()(conv2)
    conv2 = MaxPooling2D()(conv2)

    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv2)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = MaxPooling2D()(conv3)
    conv3 = Conv2D(ndim*2**2, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv3 = BatchNormalization()(conv3)
    conv3 = activation()(conv3)
    conv3 = MaxPooling2D()(conv3)

    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv3)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)
    conv4 = MaxPooling2D()(conv4)
    conv4 = Conv2D(ndim*2**3, kernel_size=(3, 3), strides=1, padding='same', kernel_initializer='he_normal')(conv4)
    conv4 = BatchNormalization()(conv4)
    conv4 = activation()(conv4)

    fully = Flatten()(conv4)
    fully = Dense(512)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(128)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(32)(fully)
    fully = BatchNormalization()(fully)
    fully = activation()(fully)
    fully = Dense(1, activation='sigmoid')(fully)

    model = tf.keras.models.Model(inputs, fully)

    return model

C. Loss Functions¶

We'll now implement the loss functions for our GAN model. As you might know that we have two loss functions, one for the generator and another for the discriminator.

For our generator, we'll use the L2/MSE loss function.
For optimization, we use the Adam optimizer with a learning rate of 0.001

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cross_entropy = tf.keras.losses.BinaryCrossentropy()
mse = tf.keras.losses.MeanSquaredError()

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output) # - tf.random.uniform( shape=real_output.shape , maxval=0.05) , real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output) # + tf.random.uniform( shape=fake_output.shape , maxval=0.05), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

def generator_loss(fake_output , real_y):
    real_y = tf.cast( real_y , 'float32' )
    return mse( fake_output , real_y )

generator_optimizer = tf.keras.optimizers.Adam()
discriminator_optimizer = tf.keras.optimizers.Adam()

generator = get_generator_model(ndim=32)
discriminator = get_discriminator_model(ndim=32)

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# Show generator graph
tf.keras.utils.plot_model(generator, rankdir="TB")

Out[ ]:

In [ ]:

# Show generator summary
generator.summary()

Model: "model_2"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            [(None, 512, 512, 1) 0                                            
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 512, 512, 32) 320         input_3[0][0]                    
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 512, 512, 32) 128         conv2d_16[0][0]                  
__________________________________________________________________________________________________
elu_27 (ELU)                    (None, 512, 512, 32) 0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, 256, 256, 32) 0           elu_27[0][0]                     
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 256, 256, 32) 9248        max_pooling2d_14[0][0]           
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 256, 256, 32) 128         conv2d_17[0][0]                  
__________________________________________________________________________________________________
elu_28 (ELU)                    (None, 256, 256, 32) 0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 128, 128, 32) 0           elu_28[0][0]                     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 128, 128, 64) 18496       max_pooling2d_15[0][0]           
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 128, 128, 64) 256         conv2d_18[0][0]                  
__________________________________________________________________________________________________
elu_29 (ELU)                    (None, 128, 128, 64) 0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 64, 64, 64)   0           elu_29[0][0]                     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 64, 64, 64)   36928       max_pooling2d_16[0][0]           
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 64, 64, 64)   256         conv2d_19[0][0]                  
__________________________________________________________________________________________________
elu_30 (ELU)                    (None, 64, 64, 64)   0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 32, 32, 64)   0           elu_30[0][0]                     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 32, 32, 128)  73856       max_pooling2d_17[0][0]           
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 32, 32, 128)  512         conv2d_20[0][0]                  
__________________________________________________________________________________________________
elu_31 (ELU)                    (None, 32, 32, 128)  0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
max_pooling2d_18 (MaxPooling2D) (None, 16, 16, 128)  0           elu_31[0][0]                     
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 16, 16, 128)  147584      max_pooling2d_18[0][0]           
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 16, 16, 128)  512         conv2d_21[0][0]                  
__________________________________________________________________________________________________
elu_32 (ELU)                    (None, 16, 16, 128)  0           batch_normalization_32[0][0]     
__________________________________________________________________________________________________
max_pooling2d_19 (MaxPooling2D) (None, 8, 8, 128)    0           elu_32[0][0]                     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 8, 8, 256)    295168      max_pooling2d_19[0][0]           
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 8, 8, 256)    1024        conv2d_22[0][0]                  
__________________________________________________________________________________________________
elu_33 (ELU)                    (None, 8, 8, 256)    0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
max_pooling2d_20 (MaxPooling2D) (None, 4, 4, 256)    0           elu_33[0][0]                     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 4, 4, 256)    590080      max_pooling2d_20[0][0]           
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 4, 4, 256)    1024        conv2d_23[0][0]                  
__________________________________________________________________________________________________
elu_34 (ELU)                    (None, 4, 4, 256)    0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 4, 4, 256)    0           elu_34[0][0]                     
__________________________________________________________________________________________________
flatten_2 (Flatten)             (None, 4096)         0           dropout_1[0][0]                  
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 1024)         4195328     flatten_2[0][0]                  
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 4096)         4198400     dense_6[0][0]                    
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 4, 4, 256)    0           dense_7[0][0]                    
__________________________________________________________________________________________________
conv2d_transpose_9 (Conv2DTrans (None, 4, 4, 256)    590080      reshape_1[0][0]                  
__________________________________________________________________________________________________
concatenate_4 (Concatenate)     (None, 4, 4, 512)    0           elu_34[0][0]                     
                                                                 conv2d_transpose_9[0][0]         
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 4, 4, 512)    2048        concatenate_4[0][0]              
__________________________________________________________________________________________________
elu_35 (ELU)                    (None, 4, 4, 512)    0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
up_sampling2d_7 (UpSampling2D)  (None, 8, 8, 512)    0           elu_35[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_10 (Conv2DTran (None, 8, 8, 256)    1179904     up_sampling2d_7[0][0]            
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 8, 8, 256)    1024        conv2d_transpose_10[0][0]        
__________________________________________________________________________________________________
elu_36 (ELU)                    (None, 8, 8, 256)    0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
up_sampling2d_8 (UpSampling2D)  (None, 16, 16, 256)  0           elu_36[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_11 (Conv2DTran (None, 16, 16, 128)  295040      up_sampling2d_8[0][0]            
__________________________________________________________________________________________________
concatenate_5 (Concatenate)     (None, 16, 16, 256)  0           elu_32[0][0]                     
                                                                 conv2d_transpose_11[0][0]        
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 16, 16, 256)  1024        concatenate_5[0][0]              
__________________________________________________________________________________________________
elu_37 (ELU)                    (None, 16, 16, 256)  0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
up_sampling2d_9 (UpSampling2D)  (None, 32, 32, 256)  0           elu_37[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_12 (Conv2DTran (None, 32, 32, 128)  295040      up_sampling2d_9[0][0]            
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 32, 32, 128)  512         conv2d_transpose_12[0][0]        
__________________________________________________________________________________________________
elu_38 (ELU)                    (None, 32, 32, 128)  0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
up_sampling2d_10 (UpSampling2D) (None, 64, 64, 128)  0           elu_38[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_13 (Conv2DTran (None, 64, 64, 64)   73792       up_sampling2d_10[0][0]           
__________________________________________________________________________________________________
concatenate_6 (Concatenate)     (None, 64, 64, 128)  0           elu_30[0][0]                     
                                                                 conv2d_transpose_13[0][0]        
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 64, 64, 128)  512         concatenate_6[0][0]              
__________________________________________________________________________________________________
elu_39 (ELU)                    (None, 64, 64, 128)  0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
up_sampling2d_11 (UpSampling2D) (None, 128, 128, 128 0           elu_39[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_14 (Conv2DTran (None, 128, 128, 64) 73792       up_sampling2d_11[0][0]           
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 128, 128, 64) 256         conv2d_transpose_14[0][0]        
__________________________________________________________________________________________________
elu_40 (ELU)                    (None, 128, 128, 64) 0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
up_sampling2d_12 (UpSampling2D) (None, 256, 256, 64) 0           elu_40[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_15 (Conv2DTran (None, 256, 256, 32) 18464       up_sampling2d_12[0][0]           
__________________________________________________________________________________________________
concatenate_7 (Concatenate)     (None, 256, 256, 64) 0           elu_28[0][0]                     
                                                                 conv2d_transpose_15[0][0]        
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 256, 256, 64) 256         concatenate_7[0][0]              
__________________________________________________________________________________________________
elu_41 (ELU)                    (None, 256, 256, 64) 0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
up_sampling2d_13 (UpSampling2D) (None, 512, 512, 64) 0           elu_41[0][0]                     
__________________________________________________________________________________________________
conv2d_transpose_16 (Conv2DTran (None, 512, 512, 32) 18464       up_sampling2d_13[0][0]           
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 512, 512, 32) 128         conv2d_transpose_16[0][0]        
__________________________________________________________________________________________________
elu_42 (ELU)                    (None, 512, 512, 32) 0           batch_normalization_42[0][0]     
__________________________________________________________________________________________________
conv2d_transpose_17 (Conv2DTran (None, 512, 512, 3)  867         elu_42[0][0]                     
==================================================================================================
Total params: 12,120,451
Trainable params: 12,115,651
Non-trainable params: 4,800
__________________________________________________________________________________________________

In [ ]:

# Show generator graph
tf.keras.utils.plot_model(discriminator, rankdir="TB")

Out[ ]:

In [ ]:

# Show discrimniator summary
discriminator.summary()

Model: "model_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         [(None, 512, 512, 3)]     0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 512, 512, 32)      2432      
_________________________________________________________________
batch_normalization_43 (Batc (None, 512, 512, 32)      128       
_________________________________________________________________
elu_43 (ELU)                 (None, 512, 512, 32)      0         
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 256, 256, 32)      0         
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 256, 256, 32)      25632     
_________________________________________________________________
batch_normalization_44 (Batc (None, 256, 256, 32)      128       
_________________________________________________________________
elu_44 (ELU)                 (None, 256, 256, 32)      0         
_________________________________________________________________
max_pooling2d_22 (MaxPooling (None, 128, 128, 32)      0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 128, 128, 64)      32832     
_________________________________________________________________
batch_normalization_45 (Batc (None, 128, 128, 64)      256       
_________________________________________________________________
elu_45 (ELU)                 (None, 128, 128, 64)      0         
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 64, 64, 64)        0         
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 64, 64, 64)        65600     
_________________________________________________________________
batch_normalization_46 (Batc (None, 64, 64, 64)        256       
_________________________________________________________________
elu_46 (ELU)                 (None, 64, 64, 64)        0         
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 32, 32, 64)        0         
_________________________________________________________________
conv2d_28 (Conv2D)           (None, 32, 32, 128)       73856     
_________________________________________________________________
batch_normalization_47 (Batc (None, 32, 32, 128)       512       
_________________________________________________________________
elu_47 (ELU)                 (None, 32, 32, 128)       0         
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 16, 16, 128)       0         
_________________________________________________________________
conv2d_29 (Conv2D)           (None, 16, 16, 128)       147584    
_________________________________________________________________
batch_normalization_48 (Batc (None, 16, 16, 128)       512       
_________________________________________________________________
elu_48 (ELU)                 (None, 16, 16, 128)       0         
_________________________________________________________________
max_pooling2d_26 (MaxPooling (None, 8, 8, 128)         0         
_________________________________________________________________
conv2d_30 (Conv2D)           (None, 8, 8, 256)         295168    
_________________________________________________________________
batch_normalization_49 (Batc (None, 8, 8, 256)         1024      
_________________________________________________________________
elu_49 (ELU)                 (None, 8, 8, 256)         0         
_________________________________________________________________
max_pooling2d_27 (MaxPooling (None, 4, 4, 256)         0         
_________________________________________________________________
conv2d_31 (Conv2D)           (None, 4, 4, 256)         590080    
_________________________________________________________________
batch_normalization_50 (Batc (None, 4, 4, 256)         1024      
_________________________________________________________________
elu_50 (ELU)                 (None, 4, 4, 256)         0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 4096)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 512)               2097664   
_________________________________________________________________
batch_normalization_51 (Batc (None, 512)               2048      
_________________________________________________________________
elu_51 (ELU)                 (None, 512)               0         
_________________________________________________________________
dense_9 (Dense)              (None, 128)               65664     
_________________________________________________________________
batch_normalization_52 (Batc (None, 128)               512       
_________________________________________________________________
elu_52 (ELU)                 (None, 128)               0         
_________________________________________________________________
dense_10 (Dense)             (None, 32)                4128      
_________________________________________________________________
batch_normalization_53 (Batc (None, 32)                128       
_________________________________________________________________
elu_53 (ELU)                 (None, 32)                0         
_________________________________________________________________
dense_11 (Dense)             (None, 1)                 33        
=================================================================
Total params: 3,407,201
Trainable params: 3,403,937
Non-trainable params: 3,264
_________________________________________________________________

3. Training The GAN¶

So finally, we'll train our GAN on the dataset, we prepared earlier.

In [ ]:

@tf.function
def train_step(input_x, real_y):
   
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        
        # Generate an image -> G( x )
        generated_images = generator( input_x , training=True)
        
        # Probability that the given image is real -> D( x )
        real_output = discriminator( real_y, training=True)
        
        # Probability that the given image is the one generated -> D( G( x ) )
        generated_output = discriminator(generated_images, training=True)
        
        # L2 Loss -> || y - G(x) ||^2
        gen_loss = generator_loss( generated_images , real_y )

        # Log loss for the discriminator
        disc_loss = discriminator_loss( real_output, generated_output )

    # Collect output
    average_loss = tf.keras.backend.mean(gen_loss)
    sum_loss = gen_loss + disc_loss

    # Compute the gradients
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    # Optimize with Adam
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

    return average_loss, sum_loss

Train Model¶

In [ ]:

# Important parameters
batch_size = 16
num_epochs = 50

In [ ]:

def load_img(fname):
    # Load gray image and scale it to [-0.5, 0.5]
    return plt.imread(fname) / 255. - 0.5

In [ ]:

from skimage.color import *
def load_img_special(fname):

    # Transform color image to different representation than RGB
    return rgb2hsv(plt.imread(fname)) -0.5
    #return rgb2hed(plt.imread(fname))

In [ ]:

# Compute number of epochs which would cover whole dataset
N = np.arange(len(fx_train))
selecters = np.array_split(np.random.permutation(N), len(N)//(batch_size))
selecters = selecters * 3 # Loop through dataset x times
N_tot = len(selecters)

aloss = []
sloss = []

for s in tqdm(range(len(selecters))):
#for s in tqdm(range(num_epochs)):
    
    # Load some images
    x_train = tf.convert_to_tensor(np.array([load_img(fx_train[s]) for i in selecters[s]]))
    y_train = tf.convert_to_tensor(np.array([load_img(fy_train[s]) for i in selecters[s]]))
    #y_train = tf.convert_to_tensor(np.array([load_img_special(fy_train[s]) for i in selecters[s]]))
    
    average_loss, sum_loss = train_step(x_train, y_train)
    
    print('Batch: {:03d}/{:03d}: {:.6f} {:.6f}'.format((s+1), N_tot, average_loss, sum_loss))
    aloss.append(average_loss)
    sloss.append(sum_loss)

Batch: 001/4218: 13.856701 15.454262
Batch: 002/4218: 10.986278 12.817921
Batch: 003/4218: 7.454744 9.181112
Batch: 004/4218: 4.402051 6.114635
Batch: 005/4218: 3.124272 4.673289
Batch: 006/4218: 1.987583 3.624164
Batch: 007/4218: 2.161666 3.797322
Batch: 008/4218: 2.023706 3.555636
Batch: 009/4218: 1.849443 3.377069
Batch: 010/4218: 1.302416 2.799319
Batch: 011/4218: 1.394053 2.852011
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Batch: 013/4218: 1.062715 2.493350
Batch: 014/4218: 1.568413 3.055525
Batch: 015/4218: 1.371664 2.865692
Batch: 016/4218: 0.908023 2.349802
Batch: 017/4218: 0.498130 1.995643
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In [ ]:

plt.figure(figsize=(12, 4))
plt.plot(aloss)
plt.plot(sloss)

Out[ ]:

[<matplotlib.lines.Line2D at 0x7ff1ef5b76d8>]

In [ ]:

plt.figure(figsize=(12, 4))
plt.plot(aloss[-500:])
plt.plot(sloss[-500:])

Out[ ]:

[<matplotlib.lines.Line2D at 0x7ff1ef2edd30>]

4. Results¶

We plot an image to see the results

In [ ]:

def revert_to_rgb(img):
    # Revert to RGB
    return np.clip((img + 0.5) * 255, 0, 255).astype('uint8')

In [ ]:

from skimage.color import *
def revert_img_special(img):
    # Transform color image to different representation
    
    img = hsv2rgb(img + 0.5)
    img = img - np.percentile(img, 1)
    img = img / np.percentile(img, 99)
    return np.clip(img*255, 0,255).astype('uint8')
    
    #return np.clip(((hed2rgb(img)-0.5)+1)*255, 0,255).astype('uint8')

In [ ]:

# Select a few images
n_examples = 20
selecter = np.random.choice(np.arange(len(fx_train)), n_examples)
x_valid = np.array([load_img(fx_train[s]) for s in selecter])
y_valid = np.array([load_img(fy_train[s]) for s in selecter])
#y_valid = np.array([load_img_special(fy_train[s]) for s in selecter])

In [ ]:

y_hat = np.array([revert_to_rgb(i) for i in tqdm(generator(x_valid).numpy())])
#y_hat = np.array([revert_img_special(i) for i in tqdm(generator(x_valid).numpy())])

In [ ]:

# Let's plot the prediction of one example
idx = 1
plt.imshow(y_hat[idx])

Out[ ]:

<matplotlib.image.AxesImage at 0x7ff1ef11f1d0>

In [ ]:

# Let's plot the original image
ex1 = revert_to_rgb(y_valid[1])
plt.imshow(ex1)

Out[ ]:

<matplotlib.image.AxesImage at 0x7ff1ef13eb38>

In [ ]:

# Let's get the prediction in transformed space
ex2 = y_hat[1]

In [ ]:

# Let's get target in transformed space
ex3 = y_valid[1]

In [ ]:

# Let's plot the RGB histograms for the target image in transformed space
plt.figure(figsize=(12, 5))
plt.title('Validation scaled')
for i in range(3):
    plt.hist(np.ravel(ex3[..., i]), bins=255, alpha=0.5)

In [ ]:

# Let's plot the RGB histograms for the target image in original space
plt.figure(figsize=(12, 5))
plt.title('Validation reversed')
for i in range(3):
    plt.hist(np.ravel(ex1[..., i]), bins=255, alpha=0.5)

In [ ]:

# Let's plot the RGB histograms for the prediction image in transformed space
plt.figure(figsize=(12, 5))
plt.title('Prediction')
for i in range(3):
    plt.hist(np.ravel(ex2[..., i]), bins=256, alpha=0.5)

In [ ]:

# Let's plot a few examples (original, target and prediction)
for i, y in enumerate(y_hat):
    fig, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True)
    axs[0].imshow(x_valid[i], cmap='gray')
    axs[0].set_title('Original')
    axs[1].imshow(revert_to_rgb(y_valid[i]))
    axs[1].set_title('Target')
    axs[2].imshow(y_hat[i])
    axs[2].set_title('Colorized')
    plt.show()

Output hidden; open in https://colab.research.google.com to view.

Save Test predictions¶

In [ ]:

!rm -rf test_color_images
!mkdir test_color_images

In [ ]:

from PIL import Image
from os.path import basename, join

# Select a few images
predictions = []

for f in tqdm(fimgs_te_x):
  out_file = join('test_color_images', basename(f))
  img = revert_to_rgb(generator(load_img(f)[None, ...]).numpy()[0])
  im = Image.fromarray(img)
  im.save(out_file)

In [ ]:

!zip -r "new_submission_hsv_16.zip" "test_color_images/"

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